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I couldn't figure out your math. I think you should try annotating this kind of code with explanatory comments. Often that will help you spot your own mistake:

The bounds object doesn't appear to be properly created. The purple p5 vertices you're rendering may be giving you a false sense of confidence, since those aren't necessarily related to what MJS sees.

It's actually a pretty simple fix, passing an array of vertices instead of individual arguments:

If I understand correctly, you want to use Vertex AI dataset inside Jupyter Notebook. I don't think that this is currently possible. You are able to export Vertex AI datasets to Google Cloud Storage in JSONL format:

Your dataset will be exported as a list of text items in JSONL format. Each row contains a Cloud Storage path, any label(s) assigned to that item, and a flag that indicates whether that item is in the training, validation, or test set.

At this moment, you can use BigQuery data inside Notebook using %%bigquery like it's mentioned in Visualizing BigQuery data in a Jupyter notebook. or use csv_read() from machine directory or GCS like it's showed in the How to read csv file in Google Cloud Platform jupyter notebook thread.

However, you can fill a Feature Request in Google Issue Tracker to add the possibility to use VertexAI dataset directly in the Jupyter Notebook which will be considered by the Google Vertex AI Team.

Move the definition of printPath outside of the body of dijkstraSSSP and, to give it access to pred, change it to accept two parameters instead of one. The additional parameter should have type int * and should point to the first element of pred.

In the call, pass pred for that new parameter. The array pred will be automatically converted to a pointer to its first element.

Can this be achieved using boost::graph?

The answer is nearly always "yes" with BGL. It's one of the most profoundly generic library designs in existence.

To my surprise, something new appeared in the type-hierarchy for adjacency_list. Apparently these days there's a named_graph mixin (actually maybe_name_graph) which uses traits on the vertex bundle to detect an "internal name".

This means you can get close. Though the vertex descriptor will not become your id, you can have O(1) lookup. And the interface has some conveniences, so you can write:

While I can't say this for certain, it looks like your issue might be that you're just not reaching some pixels. If you show the whole shader and where you dispatch it I might be able to confirm. The way that you are indexing points in the texture could be the whole problem.

The other issue I can see being possible is that you are reading and writing from the same data structure (input) - while you only read from x and z, and only write to y, this could still cause trouble as xyz are still part of a single element, which gets set all at once.

By default OpenGL assumes that the start of each row of an image is aligned to 4 bytes. This is because the GL_UNPACK_ALIGNMENT parameter by default is 4. Since the image has 3 color channels (GL_RGB), and is tightly packed the size of a row of the image may not be aligned to 4 bytes.
When a RGB image with 3 color channels is loaded to a texture object and 3*width is not divisible by 4, GL_UNPACK_ALIGNMENT has to be set to 1, before specifying the texture image with glTexImage2D:

You can either wrap the name of the class in quotes

The Wiki article doesn't specify, but that code will only solve the 'loopy' version of k-shortest-paths, where paths are not required to be simple.

The simple path version of the problem is harder: you'll want to look at something like Yen's algorithm, which does clever filtering to avoid repeated points when generating paths. Yen's algorithm can use Dijkstra's algorithm as a subroutine, but any other shortest-path algorithm can also be used instead.

There is no obvious way to modify Dijkstra's algorithm to solve the k-shortest-simple-paths problem. You'd need to track the paths in the priority queue (which is already done in your posted code), but there's an exponential upper bound on the number of times each vertex can be explored.

Here, if count[u] <= K puts an upper bound of K+1 on the number of times a vertex can be explored, which works for the non-simple path case. On the other hand, a direct modification of Dijkstra's algorithm for simple paths would, in the worst case, require you to explore a node once for each of the 2^(V-1) possibilities of which nodes had been previously visited (or possibly a slightly smaller exponential).